This program allows highly qualified undergraduate students to take graduate courses that count toward both the bachelor’s degree and master’s degree. With a head start on completing their graduate courses, students can earn their master's degree in just one additional year.
Application and Admission Information
Admission Requirements
Eligibility Requirements
- Currently a full-time, School of Computing undergraduate student
- Minimum sophomore standing
- 3.0 GPA
- Currently enrolled in the computer science, computer engineering, or software engineering major planning to pursue a master's degree in computer science
Program-Specific Admission Requirements
In your application for admission to this master's program:
- Personal Statement: Your statement should include your research interests, your objectives, and names of potential faculty advisors.
- Applicant Worksheet for Accelerated Computer Science (MS)
- Completing at least 12 undergrad credit hours in the master's degree discipline with a grade of at least B (including at least one of CSCE 310, CSCE 310H, CSCE 311, SOFT 260, SOFT 260H or RAIK 283H)
- Application fee of $25
NOT Required
- CV
- Letters of recommendation
- GRE
Enrollment Requirements
- During the senior year, students may take up to 12 credit hours of approved graduate coursework, and they will be charged the graduate tuition rate for graduate courses applied to the bachelor’s degree.
- Students retain eligibility for undergraduate scholarships and financial aid through the senior year.
- Students must have and maintain 3.0 GPA in School of Computing courses to be admitted to the master’s program.
- After completion of all bachelor’s degree requirements, students will be enrolled as a graduate student, charged the graduate tuition rate, and eligible for graduate assistantships and fellowships.
Coursework Requirements
- No more than 12 credit hours of graduate coursework may count toward both the bachelor’s and master’s degrees.
- Students must take 9-12 hours of dual-credit courses (see below) at the graduate level.
- Courses must be currently and regularly offered.
- No course substitutions are permitted.
- Students may not take 900-level courses.
- Dual-listed courses (400/800) are acceptable, but not required. If dual-listed courses are included in the Accelerated Master’s Program, students must register at the 800-level and complete all graduate-level requirements. Dual-listed courses previously taken and completed at the 400-level will not be retroactively changed to the 800-level.
- Students must complete the Graduate Credit Request for each course they want to be enrolled in at the graduate level.
- Students must attend at least 8 department colloquia, doctoral oral presentations, and/or master’s thesis presentations, a reduction from the 15 required for the standard M.S. programs.
- All other requirements are the same as the standard computer science master’s degree.
Frequently Asked Questions
Q: When can I apply?
A: As soon as you established sophomore standing. View Registrar Policy. Though specific programs may have specific timelines. Please contact the Graduate Chair.
Q: Why would a student do this?
A: Double count credits efficiently, progressing through bachelors and masters degrees simultaneously.
Q: How is this different from Graduate Credit for Undergraduates?
A: In accelerated masters programs graduate credit is applied to both the bachelors and masters degrees. In the Graduate Credit for Undergraduates undergraduate students can take graduate credit, which is not applied to the bachelors degree.
Q: Can any undergraduate student participate?
A: Students must be admitted into one of the preapproved undergraduate programs, have a 3.0 GPA, be a current and full-time student.
Q: Do I need to meet with the graduate advisor before applying?
A: It is encouraged that the student consults with the point of contact for the Masters Degree (see Graduate Chair in program lists above). Meeting with a graduate advisor is not a requirement for acceptance.
Q: How do I prepare my application materials?
A: UNL Career Services provide support for undergraduate students applying to graduate school.
Q: Am I billed at the graduate or undergraduate tuition rate?
A: Graduate credits are billed at the graduate rate.
Q: Does my scholarship and/or financial aid cover graduate tuition?
A: Contact the Office of Scholarships and Financial Aid and/or the external scholarship provider.
Q: Am I an admitted graduate student?
A: No. Students can only be admitted as a graduate student after the conferral of a bachelors degree.
Q: How will the graduate courses appear on my undergraduate degree audit?
A: They will be replacing specific undergraduate courses, as outlined above.
Q: What if I need to take a leave of absence?
A: The university policy states that after completion of all bachelor’s degree requirements, students will be enrolled in the Graduate College and must take at least 18 credit hours at the graduate level. As with any graduate student, a complete academic record is required at the time of application and admission. If the student takes a semester off from UNL, a new application may be required, with a complete academic record. Additionally, taking a semester off will have financial aid implications if student loans were used during the undergraduate degree. In the event of extenuating circumstances, students should contact their advisor or the Office of Graduate Studies about possible options.
Application Details
- When you apply, create a new account. This account will be separate from your current student account.
- Include your name and NU ID. This will affect your billing since you will be taking graduate courses.
- Select the correct program. Scroll down and select the “UNL accelerated program.” Do NOT select the M.S. program.
- You must pay the $25 application fee at the time of submission.
- After submission, your application is sent to the Office of Graduate Studies, and then to the School of Computing for review. After the school has reviewed your application, it is sent back to the Office of Graduate Studies, for the admission approval. If your application is accepted, a letter will be sent to you.
- You cannot be admitted as an undergraduate and graduate student at the same time. The Office of Graduate Studies will “future date” your graduate program. If you do not complete your undergraduate degree, contact the Office of Graduate Studies.
- If you are receiving undergraduate financial aid, acceptance to the program will not disqualify you from receiving that aid.
- Your tuition bill will be larger once you become a graduate student.
- TOEFL scores will not be required for submission since you are an undergraduate student.
- If you wish to pursue a Ph.D., you may also count required courses toward that degree with the approval of your supervisory committee. An additional application (and an additional $25 application fee) will be required for this option.
- Students must complete the application worksheet as part of the application process and identify when they are planning to take their courses (in order to receive permission codes for enrollment). The Graduate Studies Office will create the permission code and send it to you.
How to Apply
- Consult your Undergraduate Academic Advisor.
- Complete the Applicant Worksheet with your advisor.
- Decide your expected graduation timeline, so you know which master's admission term to choose on your application for admission.
- Bachelor's graduation in spring: Master's admission in summer or fall.
- Bachelor's graduation in summer: Master's admission in fall.
- Bachelor's graduation in fall: Master's admission in spring.
- Complete and submit your graduate application for admission with payment of its $25 application fee.
How to Enroll
Admitted students will be emailed permission codes and will enroll themselves in the courses identified on their worksheet.
Dual-Credit Courses
During their senior year, students will take up to 12 credit hours of approved graduate coursework that will count toward both the bachelor's and master's degrees. Students must take 9-12 hours at the graduate level from the courses listed below. NOTE: After being admitted to the accelerated master's program, students must complete the Graduate Credit Request for each course they want to be enrolled in at the graduate level.
CSCE 411/811: Data Modeling for Systems Development
Concepts of relational and object-oriented data modeling through the process of data model development including conceptual, logical and physical modeling. Techniques for identifying and creating relationships between discrete data members, reasoning about how data modeling and analysis are incorporated in system design and development, and specification paradigms for data models. Common tools and technologies for engineering systems and frameworks for integrating data. Design and analysis of algorithms and techniques for identification and exploration of data relationships, such as Bayesian probability and statistics, clustering, map-reduce, and web-based visualization.
CSCE 413/813: Database Systems
Data and storage models for database systems; entity/relationship, relational, and constraint models; relational databases; relational algebra and calculus; structured query language; Logical database design: normalization; integrity; distributed data storage; concurrency; security issues. Spatial databases and geographic information systems.
CSCE 423/823: Design and Analysis of Algorithms
Mathematical preliminaries. Strategies for algorithm design, including divide-and-conquer, greedy, dynamic programming and backtracking. Mathematical analysis of algorithms. Introduction to NP-Completeness theory, including the classes P and NP, polynomial transformations and NP-complete problems.
CSCE 428/828: Automata, Computation, and Formal Languages
Introduction to the classical theory of computer science. Finite state automata and regular languages, minimization of automata. Context free languages and pushdown automata, Turing machines and other models of computation, undecidable problems, introduction to computational complexity.
CSCE 438/838: Internet of Things
Theoretical and practical insight into the Internet of Things (IoT). Basics of IoT, including devices and sensors, connectivity, cloud processing and storage, analytics and machine learning, security, business models as well as advanced topics such as localization, synchronization, connected vehicles, and applications of IoT. Includes a group project that provides hands-on interaction with IoT.
CSCE 440/840: Numerical Analysis
Principles of numerical computing and error analysis covering numerical error, root finding, systems of equations, interpolation, numerical differentiation and integration, and differential equations. Modeling real-world engineering problems on digital computers. Effects of floating point arithmetic.
CSCE 445/845: Eye Tracking in Usability and Software Engineering
Create and evaluate new and existing human computer interfaces in the context of software engineering. Interdisciplinary applications of eye tracking in various areas of software engineering, biometrics, and psychology among others will be presented. Learn how to design, conduct, and analyze a technically sound eye tracking empirical study for software engineering problems in a group setting.
CSCE 451/851: Operating Systems Principles
Organization and structure of operating systems. Control, communication, and synchronization of concurrent processes. Processor and job scheduling. Memory organization and management including paging, segmentation, and virtual memory. Resource management. Deadlock avoidance, detection, recovery. File system concepts and structure. Protection and security. Substantial programming.
CSCE 462/862: Communication Networks
Introduction to the architecture of communication networks and the rudiments of performance modeling. Circuit switching, packet switching, hybrid switching, protocols, local and metro area networks, wide area networks and the Internet, elements of performance modeling, and network programming. Network security, asynchronous transfer mode (ATM), optical, wireless, cellular, and satellite networks, and their performance studies.
CSCE 463/863: Data and Network Security
Concepts and principles of data and network security. Focuses on practical aspects and application of crypto systems in security protocols for networks such as the Internet. Topics include: applications of cryptography and cryptosystems for digital signatures, authentication, network security protocols for wired and wireless networks, cyberattacks and countermeasures, and security in modern computing platforms.
CSCE 467/867: Testing, Verification and Analysis
In-depth coverage of problems related to software quality, and approaches for addressing them. Topics include testing techniques, dynamic and static program analysis techniques, and other approaches for verifying software qualities. Tool support for performing testing, verification, and analysis will also be studied.
CSCE 476/876: Introduction to Artificial Intelligence
Introduction to basic principles, techniques, and tools now being used in the area of machine intelligence. Languages for AI programming introduced with emphasis on LISP. Lecture topics include problem solving, search, game playing, knowledge representation, expert systems, and applications.
CSCE 477/877: Cryptography and Computer Security
Introductory course on cryptography and computer security. Topics: classical cryptography (substitution, Vigenere, Hill and permutation ciphers, and the one-time pad); Block ciphers and stream ciphers; The Data Encryption Standard; Public-key cryptography, including RSA and El-Gamal systems; Signature schemes, including the Digital Signature Standard; Key exchange, key management and identification protocols.
CSCE 478/878: Introduction to Machine Learning
Introduction to the fundamentals and current trends in machine learning. Possible applications for game playing, text categorization, speech recognition, automatic system control, date mining, computational biology, and robotics. Theoretical and empirical analyses of decision trees, artificial neural networks, Bayesian classifiers, genetic algorithms, instance-based classifiers and reinforcement learning.
CSCE 479/879: Introduction to Deep Learning
Fundamentals and current trends in deep learning. Backpropagation, activation functions, loss functions, choosing an optimizer, and regularization. Common architectures such as convolutional, autoencoders, and recurrent. Applications such as image analysis, text analysis, sequence analysis, and reinforcement learning.
